ROBIN - A Reference Observatory of Basins for INternational hydrological climate change detection
- 1UK Centre of Ecology & Hydrology, Water Resources, Wallingford, United Kingdom (stetur@ceh.ac.uk)
- 2Irish Climate Analysis and Research UnitS (ICARUS), Maynooth University, Maynooth, Ireland (jaha@ceh.ac.uk)
- *A full list of authors appears at the end of the abstract
Floods and droughts may become more severe in a warming world, potentially furthering the significant adverse impacts they cause to lives and livelihoods, infrastructure, and economies. To adapt to future changes in water quantity and regimes, we need to detect and attribute emerging trends in hydrological variables such as river flow, and we require updated projections of future flood and drought occurrence.
Numerical simulation models are used to provide such scenarios, but they are complex and highly uncertain. We can use long records of past hydrological observations to better understand and constrain these model-based projections; river flows are especially useful because they integrate climate processes over the areas covered by drainage basins.
There have been many studies of long-term changes in river flows around the world although, at a global scale (as represented by successive IPCC (Intergovernmental Panel on Climate Change) reports), confidence in observed trends remains very low. This is primarily due to the modification of river flows by human activities (e.g., presence of dams, land-cover change, channelisation and the abstraction of water for public water supplies, industry and agriculture). These human disturbances can obscure climate change signals and distort trends in river flows and in some cases lead to a complete reversal of the trends / changes caused by climate change. It is also challenging to integrate the results of various regional- and national-scale studies due to the many different methods used, hampering consistent continental- and global-scale assessments.
Therefore, to detect climate-driven trends we need to analyse river basins that are relatively undisturbed by human impacts. Recognising this, many countries have ‘Reference Hydrometric Networks’ (RHNs) consisting of catchments where river flows are measured, and where human impacts are absent or minimal. Globally however, these are sparse and there is a need for an integrated approach to advance international assessments of hydrological change on a consistent basis, such that they can provide a robust foundation for global and regional assessments such as those undertaken by the IPCC.
Here we introduce the 'Reference Observatory of Basins for INternational hydrological climate change detection' or ROBIN initiative, where we are advancing a worldwide effort to bring together a global RHN. With a growing network of partners from 20 countries spanning a broad range of climates and geographies, over the next two years ROBIN will develop a consistently defined network of near-natural catchments across the world, sharing knowledge from countries with established RHNs to enable other countries to define similar networks. ROBIN will use this network to undertake the first, truly global scale analysis of trends in river flows using minimally disturbed catchments.
With the support of international organisations, including WMO, UNESCO and IPCC, ROBIN will lay the foundations for an enduring network of catchments, to support global assessments of climate-driven trends and variability in the future.
Anne K. Fleig, Benjamin Renard, Berit Arheimer, Camila Alvarez-Garreton, Chaiwat Ekkawatpanit, Conor Murphy, Cosmo Ngondondo, Daniel Kingston, Dennis Hughes, Glenn Hodgkins, Gregor Laaha, Jan Daňhelka, Jari Uusikivi, Kerstin Stahl, Luis Medeiro, Mads-Peter Jakob Dahl, Martin Hanel, Ole Roessler, Paul Whitfield, Sarah Mager, Seth Westra, Sharad Jain, Supattra Vissesri, Walszon Lopes
How to cite: Turner, S., Barker, L., Dixon, H., Hannaford, J., Griffin, A., and Killeen, A. and the ROBIN Network: ROBIN - A Reference Observatory of Basins for INternational hydrological climate change detection , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8551, https://doi.org/10.5194/egusphere-egu22-8551, 2022.